The power and statistical behaviour of allele-sharing statistics when applied to models with two disease loci

We have evaluated the power for detecting a common trait determined by two loci, using seven statistics, of which five are implemented in the computer program SimWalk2, and two are implemented in GENEHUNTER. Unlike most previous reports which involve evaluations of the power of allele-sharing statistics for a single disease locus, we have used a simulated data set of general pedigrees in which a two-locus disease is segregating and evaluated several nonparametric linkage statistics implemented in the two programs. We found that the power for detecting linkage using the S(all) statistic in GENEHUNTER (GH, version 2.1), implemented as statistic E in SimWalk2 (version 2.82), is different in the two. The P values associated with statistic E output by SimWalk2 are consistently more conservative than those from GENEHUNTER except when the underlying model includes heterogeneity at a level of 50% where the P values output are very comparable. On the other hand, when the thresholds are determined empirically under the null hypothesis, S(all) in GENEHUNTER and statistic E have similar powe

Application of family-based association testing to assess the genotype-phenotype association involved in complex traits using single-nucleotide polymorphisms

BACKGROUND: We used the FBAT (family-based association test) software to test for association between 300 individual single-nucleotide polymorphisms and P1 (a latent trait of Kofendred Personality Disorder) in 100 simulated replicates of the Aipotu population. Using the Genetic Analysis Workshop 14 dataset, we calculated the power of FBAT to detect linkage disequilibrium on chromosome 3 (D2). Also, we calculated the false-positive rate on chromosome 1, which contains a true locus (D1) but no linkage disequilibrium was simulated between the trait and all the surrounding single-nucleotide polymorphisms. RESULTS: We were able to detect the associations between phenotype P1 and three adjacent markers B03T3056 (average p-value = 0.0002), B03T3057 (average p-value = 0.00072), and B03T3058 (average p-value = 0.0038) with power of 98%, 87%, 71% on chromosome 3, respectively. The overall false positve rate to detect association was 0.06 on chromosome 1. CONCLUSION: The power to detect a significant association in 100 nuclear families affected with the latent trait of Kofendred Personality Disorder by using FBAT was reasonable (based on 100 replicates). In the future, we will compare the performance of FBAT with alternative approaches, such as using FBAT-generalized estimating equations methods to test for association in families affected with complex traits

Application of the propensity score in a covariate-based linkage analysis of the Collaborative Study on the Genetics of Alcoholism

BACKGROUND: Covariate-based linkage analyses using a conditional logistic model as implemented in LODPAL can increase the power to detect linkage by minimizing disease heterogeneity. However, each additional covariate analyzed will increase the degrees of freedom for the linkage test, and therefore can also increase the type I error rate. Use of a propensity score (PS) has been shown to improve consistently the statistical power to detect linkage in simulation studies. Defined as the conditional probability of being affected given the observed covariate data, the PS collapses multiple covariates into a single variable. This study evaluates the performance of the PS to detect linkage evidence in a genome-wide linkage analysis of microsatellite marker data from the Collaborative Study on the Genetics of Alcoholism. Analytical methods included nonparametric linkage analysis without covariates, with one covariate at a time including multiple PS definitions, and with multiple covariates simultaneously that corresponded to the PS definitions. Several definitions of the PS were calculated, each with increasing number of covariates up to a maximum of five. To account for the potential inflation in the type I error rates, permutation based p-values were calculated. RESULTS: Results suggest that the use of individual covariates may not necessarily increase the power to detect linkage. However the use of a PS can lead to an increase when compared to using all covariates simultaneously. Specifically, PS3, which combines age at interview, sex, and smoking status, resulted in the greatest number of significant markers identified. All methods consistently identified several chromosomal regions as significant, including loci on chromosome 2, 6, 7, and 12. CONCLUSION: These results suggest that the use of a propensity score can increase the power to detect linkage for a complex disease such as alcoholism, especially when multiple important covariates can be used to predict risk and thereby minimize linkage heterogeneity. However, because the PS is calculated as a conditional probability of being affected, it does require the presence of observed covariate data on both affected and unaffected individuals, which may not always be available in real data sets

knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable

Abstract Background Testing the dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting nonlinear dependence between one continuous variable X and one binary dependent variables Y (0 or 1). Results We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/ ). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). Conclusions We concluded that knnAUC is an efficient R package to test non-linear dependence between one continuous variable and one binary dependent variable especially in computational biology area.https://deepblue.lib.umich.edu/bitstream/2027.42/146514/1/12859_2018_Article_2427.pd

Average -value of 100 replicates for each SNP on chromosome 1 (A) and chromosome 3 (B)

<p><b>Copyright information:</b></p><p>Taken from "Application of family-based association testing to assess the genotype-phenotype association involved in complex traits using single-nucleotide polymorphisms"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S68-S68.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866714.</p><p></p

New insights into the genetic mechanism of IQ in Autism Spectrum Disorders

Autism spectrum disorders (ASD) is a complex condition with a number of underlying subtypes with different symptoms and presumably different genetic causes. One important difference between these sub-phenotypes is cognition. Some forms of ASD such as Asperger’s keep cognitive development intact while the majority does not. In this study we explored genetic factors that might account for this difference. Using a case-control study based on IQ status in 1657 ASD probands, we analyzed both common and rare variants provided by the Autism Genome Project consortium via dbGAP, identified a set of genes, among them SYT1L and KIAA0319L, that are strongly associated with IQ within a population of ASD patients

The impact of redefining affection status for alcoholism on affected- sib-pair analysis

The analysis of a complex disease such as alcohol dependence requires a more precise definition of affection status. Collaborative Study on the Genetics of Alcoholism (COGA) provided a variety of qualitative and quantitative measures as well as genotype information, in addition to two criteria of affection status. To identify two groups of phenotypically 'more homogeneous' individuals among alcoholics (COGA criterion), we redefined affection status by using cluster analysis and classification and regression tree, incorporating some important covariates such as event related potentials, monoamine oxidase B activity, status of smoking, age of onset, three variables of personality assessed with the Tridimensional Personality Questionnaire and three latent class variables. With redefined affection status, we repeated nonparametric analysis by three sib pair analysis programs (SIBPAL, SIBPAIR, and BETA) using nine candidate DNA markers identified by Reich et al. [1998] and Long et al. [1998]. The goals of our analysis are 1) to confirm previous results for these nine markers with redefined affection status and 2) to compare the performance from these three programs.</p